{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,5]],"date-time":"2026-10-05T16:02:41Z","timestamp":1791216161060,"version":"4.1.0"},"reference-count":35,"publisher":"MDPI AG","issue":"2","license":[{"start":{"date-parts":[[2016,1,27]],"date-time":"2016-01-27T00:00:00Z","timestamp":1453852800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In recent years, deep learning has been widely studied for remote sensing image analysis. In this paper, we propose a method for remotely-sensed image classification by using sparse representation of deep learning features. Specifically, we use convolutional neural networks (CNN) to extract deep features from high levels of the image data. Deep features provide high level spatial information created by hierarchical structures. Although the deep features may have high dimensionality, they lie in class-dependent sub-spaces or sub-manifolds. We investigate the characteristics of deep features by using a sparse representation classification framework. The experimental results reveal that the proposed method exploits the inherent low-dimensional structure of the deep features to provide better classification results as compared to the results obtained by widely-used feature exploration algorithms, such as the extended morphological attribute profiles (EMAPs) and sparse coding (SC).<\/jats:p>","DOI":"10.3390\/rs8020099","type":"journal-article","created":{"date-parts":[[2016,1,28]],"date-time":"2016-01-28T02:19:45Z","timestamp":1453947585000},"page":"99","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":221,"title":["Hyperspectral Imagery Classification Using Sparse Representations of Convolutional Neural Network Features"],"prefix":"10.3390","volume":"8","author":[{"given":"Heming","family":"Liang","sequence":"first","affiliation":[{"name":"Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Qi","family":"Li","sequence":"additional","affiliation":[{"name":"Institute of Remote Sensing and GIS, Peking University, Beijing 100871, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2016,1,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1137\/060657704","article-title":"From sparse solutions of systems of equations to sparse modeling of signals and images","volume":"51","author":"Bruckstein","year":"2009","journal-title":"SIAM Rev."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"2056","DOI":"10.1109\/JSTARS.2013.2264720","article-title":"A nonlocal weighted joint sparse representation classification method for hyperspectral imagery","volume":"7","author":"Zhang","year":"2014","journal-title":"IEEE J. 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